Test cases are valuable assets for maintaining software quality. State-of-the-art automated test generation techniques typically focus on maximizing program branch coverage or translating focal methods into test code. However, in contrast to branch coverage or code-to-test translation, practical tests are written out of the need to validate whether a requirement has been fulfilled. Specifically, each test usually reflects a developer’s \textit{validation intention} for a program function, regarding (1) \textit{what is the test scenario of a program function?} and (2) \textit{what is expected behavior under such a scenario?} Without taking such intention into account, generated tests are less likely to be adopted in practice.

In this work, we propose IntentionTest, which generates project-specific tests given the description of validation intention. The design is motivated by two insights: (1) \textbf{rationale insight}: the description of validation intention regarding scenario description and behavioral expectation, compared to coverage and focal code, carries more crucial information about \ul{\textit{what to test}}; and (2) \textbf{technical insight}: practical test code exhibits high duplication, indicating that existing tests are highly reusable for \ul{\textit{how to test}}. Therefore, IntentionTest adopts a retrieval-and-edit manner. First, given a focal code and a description of validation intention consisting of a test objective with test precondition and expected results, IntentionTest retrieves a reusable test in the project as the test reference. Then, IntentionTest edits the test reference with an LLM regarding the validation intention toward the target test. To guarantee that the target test can have a project-specific test prefix and a relevant test assertion, IntentionTest further explores the software project to identify \textit{crucial code facts} (i.e., relevant API/code to call and global variables to refer to in the test) as important context for the test generation. We extensively evaluate IntentionTest against four baselines (TELPA, DA, ChatTester, and EvoSuite) on 3,680 test cases from 12 open-source projects. Compared to state-of-the-art baselines, with a given validation intention, IntentionTest can (1) generate tests far more semantically relevant to ground-truth tests by (i) killing 28.1% to 37.6% more common mutants and (ii) sharing 16.9% to 23.9% more common coverage; and (2) generate 23.7% to 49.0% more successful passing tests.